APGAR score, Bayi berat lahir re PERBEDAAN NILAI APGAR SCORE BAYI BERAT LAHIR RENDAH CUKUP BULAN DAN BAYI BERAT LAHIR RENDAH TIDAK CUKUP BULAN

Tingkat kesehatan ibu dan anak merupakan salah satu indikator kemajuan suatu Negara dalam bentuk pelayanan kesehatan (Prawirohardjo, 2005). Namun, Bayi Baru Lahir (BBL) yang mengalami masalah akan berpotensi mengancam jiwanya (Depkes, 2010). Oleh sebab itu penilaian awal pada bayi harus segera dilakukan, sebab bayi yang tidak cukup bulan sering terancam bahaya maut khususnya jika kelahiran bayi terlalu awal. Salah satu penilaian yang dilakukan yaitu nilai Apgar, umumnya dilaksanakan pada 1 menit pertama 5 menit kedua sesudah bayi lahir. Penelitian ini dilakukan pada bayi dengan berat lahir rendah yang cukup bulan dan bayi dengan berat lahir rendah tidak cukup bulan. Penelitian ini merupakan penelitian kuantitatif dengan metode komparatif yang bertujuan untuk melihat perbedaan nilai Apgar score BBLR cukup bulan dan tidak cukup bulan. Hasil dianalisis secara univariat dan bivariat dengan menggunakan uji statistik T independen. Instrumen penelitian dilakukan menggunakan lembar observasi. Hasil penelitian diketahui bahwa rata-rata nilai Apgar score bayi baru lahir rendah 5,19 dengan standar deviasi 1,610. Dari estimasi interval bahwa 95% rata-rata Apgar score BBLR 4,83 sampai dengan 5,55. Umur kehamilan cukup bulan rata-rata 5,68 sebanyak 37, umur kehamilan tidak cukup bulan rata-rata 4,76 sebanyak 42 dengan P value 0,011. Penilaian Apgar score pada masing-masing bayi dengan BBLR cukup bulan dan BBLR tidak cukup bulan memiliki nilai Apgar score yang berbeda-beda antara satu dengan yang lainnya. Penilaian Apgar score agak rendah biasanya ditemukan pada beberapa bayi baru lahir, terutama bayi yang lahir dari ibu hamil dengan resiko tinggi, SC, atau ibu yang memiliki komplikasi pada saat hamil. The level of mother and kid healthy is one of progress indicator about healthy services in the country ( Prawiroharjo, 2005). However, the newest baby born who have a problem potention to threaten their soul ( Healthy Department ,2010). So, the first evaluation for the baby should be done, because of the baby who not enough month often threat to die especially very early born. One of the evaluation done is APGAR. Generally it is do at the first minute, the second five minutes after the baby born. These evaluation do to low weight baby born enough month and weight baby born not enough month. These evaluation is quantitative evaluation with comparative evaluation who have a purpose to see the difference low weight baby born enough month and weight baby born not enough month APGAR score. The result of dianalysis univariat and bivariat by using T independent statistic test. The evaluation instrument do by using observation sheet. Result of the evaluation seen if APGAR score is low baby born 5,19 by deviation standard 1,610. From the estimation interval is 95% low weight baby born enough month APGAR score 4,83 to 5,55. Age pregnancy enough of month about 5,68 as much as 37..Age pregnancy not enough of month about 4,76 as much as 42 by P value 0,011. The evaluation of APGAR score for every baby by enough and not enough month having difference score between one and others. Lower evaluation APGAR score usually found to some of the newest baby born, especially baby born from pregnancy mother with high risk, SC, or mother who has complication when pregnancy.

The Low Beta Anomaly and Estimation Interval

The purpose of this study was to assess the nature of the relationship between equity beta, and post-estimation return. Specifically, this study sought to address the validity and persistence of the low-beta anomaly across multiple beta estimation intervals. Within the twenty-year sample period from January of 1994 to December of 2013 this research covered ten different beta estimation intervals to determine whether a statistically significant and theoretically consistent relationship existed between equity beta and post-estimation realized return. This research provided two basic conclusions: First, the low-beta anomaly is not robust across multiple beta estimation intervals. Second, with any test of the relationship between beta and return the choice of beta estimation interval matters. Different estimation intervals sometimes provide contradictory empirical results for the same period.

A Bimodal Model to Estimate Dynamic Metropolitan Population by Mobile Phone Data

Accurate, real-time and fine-spatial population distribution is crucial for urban planning, government management, and advertisement promotion. Limited by technics and tools, we rely on the census to obtain this information in the past, which is coarse and costly. The popularity of mobile phones gives us a new opportunity to investigate population estimation. However, real-time and accurate population estimation is still a challenging problem because of the coarse localization and complicated user behaviors. With the help of the passively collected human mobility and locations from the mobile networks including call detail records and mobility management signals, we develop a bimodal model beyond the prior work to better estimate real-time population distribution at metropolitan scales. We discuss how the estimation interval, space granularity, and data type will influence the estimation accuracy, and find the data collected from the mobility management signals with the 30 min estimation interval performs better which reduces the population estimation error by 30% in terms of Root Mean Square Error (RMSE). These results show us the great potential of using bimodal model and mobile phone data to estimate real-time population distribution.

Universal Scale of Intelligence Estimates (USIE): Representing Intelligence Estimated From Level of Education

In clinical neuropsychology, it is often necessary to estimate a patient’s premorbid level of cognitive functioning in order to evaluate whether his scores on cognitive tests should be considered abnormal. In practice, test results from before the onset of brain pathology are rarely available, and the patient’s level of education is used instead as an estimate of his premorbid level. Unfortunately, level of education may be expressed on many different scales of education, which are difficult to use interchangeably. Here, we introduce a new scale that has the capacity to replace existing scales and can be used interchangeably with any of them: the Universal Scale of Intelligence Estimates (USIE). To achieve this, we propose to map all levels of existing educational scales to standard IQ scores. This USIE point estimate is supplemented with an estimation interval. We assert that USIE offers some important benefits for clinical practice and research.

Chebyshev Similarity Match between Uncertain Time Series

In real application scenarios, the inherent impreciseness of sensor readings, the intentional perturbation of privacy-preserving transformations, and error-prone mining algorithms cause much uncertainty of time series data. The uncertainty brings serious challenges for the similarity measurement of time series. In this paper, we first propose a model of uncertain time series inspired by Chebyshev inequality. It estimates possible sample value range and central tendency range in terms of sample estimation interval and central tendency estimation interval, respectively, at each time slot. In comparison with traditional models adopting repeated measurements and random variable, Chebyshev model reduces overall computational cost and requires no prior knowledge. We convert Chebyshev uncertain time series into certain time series matrix; therefore noise reduction and dimensionality reduction are available for uncertain time series. Secondly, we propose a new similarity matching method based on Chebyshev model. It depends on overlaps between two sample estimation intervals and overlaps between central tendency estimation intervals from different uncertain time series. At the end of this paper, we conduct an extensive experiment and analyze the results by comparing with prior works.